Journal
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS
Volume 12, Issue 4, Pages -Publisher
WILEY
DOI: 10.1002/wics.1486
Keywords
approximate Bayesian computation; neural density estimation; synthetic likelihood
Categories
Funding
- Australian Research Council Center of Excellence for Mathematical and Statistical Frontiers
- MIUR [20154X8K23-SH3]
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This paper provides a review of approximate Bayesian computation (ABC) methods for carrying out Bayesian posterior inference, through the lens of density estimation. We describe several recent algorithms and make connection with traditional approaches. We show advantages and limitations of models based on parametric approaches and we then draw attention to developments in machine learning, which we believe have the potential to make ABC scalable to higher dimensions and may be the future direction for research in this area. This article is categorized under: Algorithms and Computational Methods < Algorithms Statistical and Graphical Methods of Data Analysis < Bayesian Methods and Theory Statistical Models < Simulation Models
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